class_docstr: El-Attar-Vidyasagar-Dutta [1]_ objective function.
  
  This class defines the El-Attar-Vidyasagar-Dutta function global
  optimization problem. This is a multimodal minimization problem defined as
  follows:
  
  .. math::
  
     f_{\text{ElAttarVidyasagarDutta}}(x) = (x_1^2 + x_2 - 10)^2
     + (x_1 + x_2^2 - 7)^2 + (x_1^2 + x_2^3 - 1)^2
  
  
  with :math:`x_i \in [-100, 100]` for :math:`i = 1, 2`.
  
  *Global optimum*: :math:`f(x) = 1.712780354` for
  :math:`x= [3.40918683, -2.17143304]`
  
  .. [1] Gavana, A. Global Optimization Benchmarks and AMPGO retrieved 2015
code: def __init__(self, dimensions=2):
      Benchmark.__init__(self, dimensions)
  
      self._bounds = list(zip([-100.0] * self.N, [100.0] * self.N))
      self.custom_bounds = [(-4, 4), (-4, 4)]
  
      self.global_optimum = [[3.40918683, -2.17143304]]
      self.fglob = 1.712780354
  
imports: from numpy import abs, asarray, cos, exp, arange, pi, sin, sqrt, sum
  from .go_benchmark import Benchmark
label_desc: Use image node_id 1 to create a new ElAttarVidyasagarDutta object from inherited base classes: Benchmark with example: obj = ElAttarVidyasagarDutta(dimensions)